• Proceedings of the Korean Vacuum Society Conference
• /
• 2014.02a
• /
• pp.289.1-289.1
• /
• 2014

In this study, we made a organic thin film device in MIS(Metal-Insulator-Semiconductor) structure by using PVP (Poly vinyl phenol) as a insulating layer, and CdSe/ZnS nano particles which have a core/shell structure inside. We dissolved PVP and PMF in PGMEA, organic solvent, then formed a thin film through a spin coating. After that, it was cross-linked by annealing for 1 hour in a vacuum oven at $185^{\circ}C$. We operated FTIR measurement to check this, and discovered the amount of absorption reduced in the wave-length region near 3400 cm-1, so could observe decrease of -OH. Boonton7200 was used to measure a C-V relationship to confirm a properties of the nano particles, and as a result, the width of the memory window increased when device including nano particles. Additionally, we used HP4145B in order to make sure the electrical characteristics of the organic thin film device and analyzed a conduction mechanism of the device by measuring I-V relationship. When the voltage was low, FNT occurred chiefly, but as the voltage increased, Schottky Emission occurred mainly. We synthesized CdSe/ZnS and to confirm this, took a picture of Si substrate including nano particles with SEM. Spherical quantum dots were properly made. Due to this study, we realized there is high possibility of application of next generation memory device using organic thin film device and nano particles, and we expect more researches about this issue would be done.

• Journal of Korean Society for Quality Management
• /
• v.47 no.3
• /
• pp.497-508
• /
• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.2
• /
• pp.53-58
• /
• 2021

In this paper, an alternative finite memory structure(FMS) smoothing filter is developed for discrete-time state-space model with a control input. To obtain the FMS smoothing filter, unbiasedness will be required beforehand in addition to a performance criteria of minimum variance. The FMS smoothing filter is obtained by directly solving an optimization problem with the unbiasedness constraint using only finite measurements and inputs on the most recent window. The proposed FMS smoothing filter is shown to have intrinsic good properties such as deadbeat and time-invariance. In addition, the proposed FMS smoothing filter is shown to be equivalent to existing FMS filters according to the delay length between the measurement and the availability of its estimate. Finally, to verify intrinsic robustness of the proposed FMS smoothing filter, computer simulations are performed for a temporary model uncertainty. Simulation results show that the proposed FMS smoothing filter can be better than the standard FMS filter and Kalman filter.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.11
• /
• pp.2548-2554
• /
• 2010

This paper describes a design of fast Fourier transform(FFT) processor for EEG(electroencephalogram) signal analysis for health care services. Hamming window function with 1/2 overlapping is adopted to perform short-time FFT(ST-FFT) of a long period EEG signal occurred in real-time. In order to analyze efficiently EEG signals which have frequency characteristics in the range of 0 Hz to 100 Hz, a 256-point FFT processor is designed, which is based on a single-memory bank architecture and the radix-4 algorithm. The designed FFT processor has been verified by FPGA implementation, and has high accuracy with arithmetic error less than 2%.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.515-520
• /
• 2009

This paper presents the software design of RFID Educational system based on using 900MHz air interface between the reader and the tag. Software of the reader and active tag is developed on embedded environment and the software of PC controlling the reader is on window OS. ATmega128 processor is used for H/W of the reader and active tag, and C language is used for their developing. Programming on window OS used MFC. Main functions of this system are to control tag containing EPC global Data by PC through the reader, to obtain information of tag through the internet and to read/write data on tag memory. Software design of 900MHz RFID educational system is done on the basis of these functions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.88-91
• /
• 2010

This paper describes a design of fast Fourier transform(FFT) processor for EEG(electroencephalogram) signal analysis for health care services. Hamming window function with 1/2 overlapping is adopted to perform short-time FFT(ST-FFT) of a long period EEG signal occurred in real-time. In order to analyze efficiently EEG signals which have frequency characteristics in the range of 0 Hz to 100 Hz, a 256-point FFT processor based on single-memory bank architecture and radix-4 algorithm is designed. The designed FFT processor has high accuracy with arithmetic error less than 3%.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.11
• /
• pp.1796-1801
• /
• 2001

For micro-machines, very few design methodologies based on optimization hale been developed so far. To overcome the difficulties of design optimization of micro-machines, the search method for multi-dimensional design window (DW)s is proposed. The proposed method is defined as areas of satisfactory design solutions in a design parameter space, using both continuous evolutionary algorithms (CEA) and the modified K-means clustering algorithm . To demonstrate practical performance of the proposed method, it was applied to an optimal shape design of micro electrostatic actuator of optical memory. The shape design problem has 5 design parameters and 5 objective functions, and finally shows 4 specific design shapes and design characters based on the proposed DWs.

• Journal of Information Processing Systems
• /
• v.6 no.4
• /
• pp.481-500
• /
• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• The KIPS Transactions:PartD
• /
• v.12D no.3 s.99
• /
• pp.335-344
• /
• 2005

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.3
• /
• pp.509-516
• /
• 2008

Recently, the operator modules to control external devices are concerned about automatic management system to process continuously changed signals. These signals are the stream data of which characteristics are several numbers. a short report interval and asynchronous report time. It is necessary that the system brings about high accuracy and real time process for stream data. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query at a past time, the sliding window query from a past time to current. In this paper, we propose the efficient method to manage the above signals by using a file structured database in small-size operating systems. We also propose a query model to accommodate various queries including the sliding window query. The file database in the QNX adopts a delta version and a shared memory buffering method for the resource limit of a small storage and a low computing power.